Synthesizing genome regulation data with vote-counting
Authors: Martin Fischer, Steve Hoffmann
Journal: Trends in Genetics (2022)

An avalanche of omics data generated by multiple high-throughput experiments provides an opportunity to validate results and to make new discoveries.While typically regarded as a measure of last resort for meta-analyses, naive vote-counting approaches can have considerable advantages when synthesizing genome regulation data and their simplicity enables a more significant number of researchers to use meta-analyses.Powerful genomic signatures can be extracted via meta-analyses. Vote-counting-based procedures enable the user to score and rank the results to intuitively balance the tradeoff between specificity and sensitivity.Vote-counting-based scores and ranks allow for a swift integration of diverse data types (e.g., ChIP-seq-derived transcription factor binding data and RNA-seq-derived differential gene expression data). The increasing availability of high-throughput datasets allows amalgamating research information across a large body of genome regulation studies. Given the recent success of meta-analyses on transcriptional regulators, epigenetic marks, and enhancer:gene associations, we expect that such surveys will continue to provide novel and reproducible insights. However, meta-analyses are severely hampered by the diversity of available data, concurring protocols, an eclectic amount of bioinformatics tools, and myriads of conceivable parameter combinations. Such factors can easily bar life scientists from synthesizing omics data and substantially curb their interpretability. Despite statistical challenges of the method, we would like to emphasize the advantages of joining data from different sources through vote-counting and showcase examples that achieve a simple but highly intuitive data integration. The increasing availability of high-throughput datasets allows amalgamating research information across a large body of genome regulation studies. Given the recent success of meta-analyses on transcriptional regulators, epigenetic marks, and enhancer:gene associations, we expect that such surveys will continue to provide novel and reproducible insights. However, meta-analyses are severely hampered by the diversity of available data, concurring protocols, an eclectic amount of bioinformatics tools, and myriads of conceivable parameter combinations. Such factors can easily bar life scientists from synthesizing omics data and substantially curb their interpretability. Despite statistical challenges of the method, we would like to emphasize the advantages of joining data from different sources through vote-counting and showcase examples that achieve a simple but highly intuitive data integration. Mammalian genomes comprise billions of DNA base pairs and thousands of genes. Fueled by increasingly cheap technologies, research into these complex genomes' organization, function, and regulation has triggered an avalanche of high-throughput experiments [1.Reuter J.A. et al.High-throughput sequencing technologies.Mol. Cell. 2015; 58: 586-597Abstract Full Text Full Text PDF PubMed Scopus (760) Google Scholar,2.Fan J.-B. et al.Highly parallel genomic assays.Nat. Rev. Genet. 2006; 7: 632-644Crossref PubMed Scopus (327) Google Scholar]. On the flip side, the deluge of data and data types requires increasingly sophisticated integration and in silico analysis techniques. Typical research projects frequently involve combinations of experiments to profile the transcriptome, DNA binding landscapes of transcription factors, epigenetic marks, or maps of enhancer:gene associations. Among those protocols, gene expression experiments assume a unique role. Because of the comparably large body of pre-existing knowledge, transcriptomic differences are often easier to interpret and can be linked better to phenotypic variation (e.g., between species, tissues, and single cells). By contrast, the interpretation of methylation differences, for example, is usually less straightforward. However, even for transcriptomic data, knowledge extraction may become complicated when large sets of (differentially) expressed genes (DEGs) need to be analyzed. Sources of variability, technical and biological, further add to this problem [3.Hansen K.D. et al.Sequencing technology does not eliminate biological variability.Nat. Biotechnol. 2011; 29: 572-573Crossref PubMed Scopus (142) Google Scholar]. Technical variability is typically hard to control and arises from differences of reagents, measurement systems, or the handling of samples. However, biological variability from patient to patient, animal to animal, and cell to cell is also a well-known effect partially reflecting gene expression stochasticity [4.Chalancon G. et al.Interplay between gene expression noise and regulatory network architecture.Trends Genet. 2012; 28: 221-232Abstract Full Text Full Text PDF PubMed Scopus (185) Google Scholar]. These phenomena are well documented to impede reproducibility for both microarray [5.Draghici S. et al.Reliability and reproducibility issues in DNA microarray measurements.Trends Genet. 2006; 22: 101-109Abstract Full Text Full Text PDF PubMed Scopus (472) Google Scholar, 6.Shi L. et al.The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies.BMC Bioinformatics. 2008; 9: S10Crossref PubMed Scopus (194) Google Scholar, 7.Leek J.T. et al.Tackling the widespread and critical impact of batch effects in high-throughput data.Nat. Rev. Genet. 2010; 11: 733-739Crossref PubMed Scopus (1272) Google Scholar] and sequencing technologies [8.Tarazona S. et al.Differential expression in RNA-seq: a matter of depth.Genome Res. 2011; 21: 2213-2223Crossref PubMed Scopus (1136) Google Scholar,9.Love M.I. et al.Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation.Nat. Biotechnol. 2016; 34: 1287-1291Crossref PubMed Scopus (84) Google Scholar]. While the experimental design should adequately address these problems [e.g., by taking group-specific sample similarities into account (blocking) or increasing sample sizes], this is often not achieved in practice. The balancing act between financial pressures and good experimental planning (e.g., sufficient replicate numbers, suitable sequencing depth, or meaningful validations) can easily be thrown off balance. Yet, even in studies with optimal designs, the results of high-throughput data analysis can be misleading [10.Nuzzo R. Scientific method: statistical errors.Nature. 2014; 506: 150-152Crossref PubMed Scopus (1066) Google Scholar]. There is a widespread consensus that it is necessary to obtain independent verification. The large and increasing number of published high-throughput studies provides an opportunity to validate results and improve power by data integration, for example, using meta-analyses. Naturally, the strength of a scientific assertion increases with the number and quality of studies arriving at the same conclusion. According to Glass [11.Glass G.V. Primary, secondary, and meta-analysis of research.Educ. Res. 1976; 5: 3-8Crossref Google Scholar], meta-analysis refers to the analysis of analyses and may be defined as the statistical investigation of a large collection of results from individual studies for the purpose of integrating the findings. Thus, it is distinct from the primary analysis and the secondary analysis that scrutinizes an existing dataset with new methods. Statistical meta-analysis provides the means to aggregate independent studies to allow statements about common parameters of interest. In principle, meta-analyses aim to obtain improved estimates of unknown effect sizes or to discover recurrent patterns by gathering data from multiple sources. Indeed, if carefully designed and conducted, meta-analyses can provide more accurate estimates than each of its constituent studies [12.Sweeney T.E. et al.Methods to increase reproducibility in differential gene expression via meta-analysis.Nucleic Acids Res. 2017; 45e1Crossref PubMed Scopus (86) Google Scholar]. Therefore, meta-analyses are frequently part of systematic literature reviews (i.e., studies that amalgamate evidence from original articles to answer a specific and well-defined question). However, the benefits of meta-analyses are not limited to better estimates or the mere gain of statistical power. In our opinion, their most critical advantage is the possibility to assess the generalizability of findings made in individual studies and to identify previously unknown subgroups. While meta-analyses are well-established tools in medicine and biology (e.g., to integrate clinical [13.Sacks H.S. et al.Meta-analyses of randomized controlled trials.N. Engl. J. Med. 1987; 316: 450-455Crossref PubMed Scopus (1038) Google Scholar,14.Moher D. et al.Improving the quality of reports of meta-analyses of randomised controlled trials: the QUOROM statement.Lancet. 1999; 354: 1896-1900Abstract Full Text Full Text PDF PubMed Scopus (3990) Google Scholar] or genome-wide association studies [15.Panagiotou O.A. et al.The power of meta-analysis in genome-wide association studies.Annu. Rev. Genomics Hum. Genet. 2013; 14: 441-465Crossref PubMed Scopus (92) Google Scholar]), they are quickly gaining momentum in areas where new data have just begun to accumulate (e.g., for single-cell RNA-seq [16.Butler A. et al.Integrating single-cell transcriptomic data across different conditions, technologies, and species.Nat. Biotechnol. 2018; 36: 411-420Crossref PubMed Scopus (4969) Google Scholar] or metabolomics [17.Chong J. et al.MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.Nucleic Acids Res. 2018; 46: W486-W494Crossref PubMed Scopus (2564) Google Scholar] analyses). Notably, jointly analyzing data from different sources also works to the level of methods. By integrating the results of various analysis tools obtained with the same dataset, 'wisdom of crowds'-approaches can tackle complex questions in the study of gene regulation networks [18.Marbach D. et al.Wisdom of crowds for robust gene network inference.Nat. Methods. 2012; 9: 796Crossref PubMed Scopus (1180) Google Scholar]. Formally, meta-analyses can be described in five stages (problem formulation, data collection, data evaluation, data analysis, and interpretation) and we would like to refer to [19.Hartung J. et al.Statistical Meta-Analysis with Applications. Wiley, 2008Crossref Scopus (354) Google Scholar] for a detailed and intuitive explanation of the details. In this context, we would like to point out that imprecise formulations, imbalances in the data collection, or a publication bias (insignificant results are usually not published) may impede the validity of a meta-analysis. While generally beneficial, the increasing number of meta-analysis studies conducted in the past two decades have also revealed some critical shortcomings, which, for instance, can lead to discordant results between meta-analyses on the same topic or question [20.Jadad A.R. et al.A guide to interpreting discordant systematic reviews.Can. Med. Assoc. J. 1997; 156: 1411-1416PubMed Google Scholar,21.LeLorier J. et al.Discrepancies between meta-analyses and subsequent large randomized, controlled trials.N. Engl. J. Med. 1997; 337: 536-542Crossref PubMed Scopus (1012) Google Scholar]. In the case of genome regulation studies, however, publications often comprise genome-wide data (e.g., expression values for all genes of a genome), while focusing only on a smaller subset of the results. Moreover, data and resource publications further ameliorate the publication bias problem. In the optimal case, meta-analyses use effect sizes (i.e., quantitative measures capturing relevant information from each survey to enable integration). Measured in response to some treatment or obtained by comparing conditions, effect sizes are typically given in terms of standardized mean differences, correlations, or fold changes. In addition to being comparable across the original studies, effect sizes should be consistently interpretable across all measures and variables. They shall express the direction and magnitude of the response [19.Hartung J. et al.Statistical Meta-Analysis with Applications. Wiley, 2008Crossref Scopus (354) Google Scholar]. Although controversially discussed, we believe that seemingly naive vote-counting approaches [22.Tseng G.C. et al.Comprehensive literature review and statistical considerations for microarray meta-analysis.Nucleic Acids Res. 2012; 40: 3785-3799Crossref PubMed Scopus (278) Google Scholar] may play a more critical role in the future. Vote-counting captures only the direction of an effect [significantly positive (+1), significantly negative (–1), or nonsignificant (0)]. For instance, one could compare the number of studies reporting the significant differential regulation of a given gene versus the number of studies that do not report such an effect for a given experimental treatment. Subsequently, a decision is made using a minimum vote cut-off: if a certain percentage of studies support the differential expression, the gene is deemed to be influenced by the treatment. In the literature, there is a broad consensus that vote-counting procedures are a measure of last resort in cases where the data are incomplete and need to be applied and interpreted with great care (Box 1). Specifically, vote-counting can become necessary when studies either only report the results of hypothesis tests (e.g., P values for DEGs) or the respective conclusions obtained (e.g., a list of significantly dysregulated genes) [19.Hartung J. et al.Statistical Meta-Analysis with Applications. Wiley, 2008Crossref Scopus (354) Google Scholar]. Even if the available data are complete and comprise all necessary information to recalculate effect size estimates such as means or correlations, vote-counting can have considerable advantages. Given the sheer size and number of the datasets available today, recalculations for multiple studies can quickly become unfeasible and cumbersome. While vote-counting data need to be interpreted carefully, it provides a most intuitive measure for the number of studies that agree on a specific finding (e.g., the upregulation of a gene following some treatment). Ultimately, the simplicity of vote-counting enables a more significant number of researchers to use meta-analyses.Box 1Limitations of vote-counting meta-analysesVote-counting procedures are critically discussed in the literature and there is an accord that they should be applied with caution. Vote-counting yields no information on the magnitude of an effect and there is no way to assess the required homogeneity of effects across the included studies. Because of this lack of knowledge, naive vote-counting studies are frequently regarded as inferior and have sparked controversial discussions [58.Friedman L. Why vote-count reviews don't count.Biol. Psychiatry. 2001; 49: 161-162Abstract Full Text Full Text PDF Scopus (21) Google Scholar]. For instance, Friedman points out that the incorporation of low-powered studies can easily lead to the wrong conclusions (i.e., the failure to find a statistically significant result) [50.Gentles A.J. et al.The prognostic landscape of genes and infiltrating immune cells across human cancers.Nat. Med. 2015; 21: 938-945Crossref PubMed Scopus (1893) Google Scholar]. This important caveat is owed to the fact that all studies, irrespective of their sample size and statistical precision, have the same influence on the procedure: exactly one vote. In fact, if the average power of the studies used for vote-counting is smaller than the chosen minimum vote cut-off, then the probability that the vote count leads to the right decision approaches zero as more studies are included [59.Hedges L. Olkin I. Vote-counting methods.in: Statistical Methods for Meta-Analysis. Academic Press, 1985: 48-52Crossref Google Scholar]. Therefore, vote-counting is especially prone to miss small effects if the average study is underpowered. Also, the selection of the cut-off is left to the analyst and may be difficult to justify. Ultimately, the assessment of the biological relevance needs to be done with alternative methods. While effect size procedures have clear advantages and should be given preference, vote-counting may still be an important and legitimate analysis tool [60.Bushman B.J. Wang M.C. Vote-counting procedures in meta-analysis.in: Cooper H. The Handbook of Research Synthesis and Meta-Analysis. Russell Sage Foundation, 2009: 207-220Google Scholar]. Most obviously, this is the case when effect sizes are not reported and the available raw data do permit the calculation of effect size estimates. In any case, a strong vote-counting result can help researchers to substantiate their research hypothesis or to detect novel interactions.In genome regulation studies, the situation can be more cumbersome. For example, in semi-quantitative ChIP-seq studies, effect sizes calculated from the number of aligned reads to a genomic locus (e.g., fold enrichments) are hard to compare across laboratory environments (e.g., because of different instruments or protocol variations) and challenging to interpret. Therefore, such studies are designed to merely establish whether a certain signal is present or absent at a specific locus. In our view, performing optimal effect size-based meta-analyses for next-generation sequencing-based experiments would require the rigorous use of spike-ins (usually not available) and a complete recalculation (e.g., read preprocessing, alignment, peak or differential expression calling) based on the raw data if it is available. Such practical considerations underscore the potential usability of badly reputed vote-counting strategies. While it is clear that vote-counting results need to be interpreted carefully, we believe that they are indispensable tools to summarize available data, develop new hypotheses, and trigger validation experiments. Recent work has proposed more sophisticated forms of vote-counting by combining the counts with effect size estimates [60.Bushman B.J. Wang M.C. Vote-counting procedures in meta-analysis.in: Cooper H. The Handbook of Research Synthesis and Meta-Analysis. Russell Sage Foundation, 2009: 207-220Google Scholar]. Vote-counting procedures are critically discussed in the literature and there is an accord that they should be applied with caution. Vote-counting yields no information on the magnitude of an effect and there is no way to assess the required homogeneity of effects across the included studies. Because of this lack of knowledge, naive vote-counting studies are frequently regarded as inferior and have sparked controversial discussions [58.Friedman L. Why vote-count reviews don't count.Biol. Psychiatry. 2001; 49: 161-162Abstract Full Text Full Text PDF Scopus (21) Google Scholar]. For instance, Friedman points out that the incorporation of low-powered studies can easily lead to the wrong conclusions (i.e., the failure to find a statistically significant result) [50.Gentles A.J. et al.The prognostic landscape of genes and infiltrating immune cells across human cancers.Nat. Med. 2015; 21: 938-945Crossref PubMed Scopus (1893) Google Scholar]. This important caveat is owed to the fact that all studies, irrespective of their sample size and statistical precision, have the same influence on the procedure: exactly one vote. In fact, if the average power of the studies used for vote-counting is smaller than the chosen minimum vote cut-off, then the probability that the vote count leads to the right decision approaches zero as more studies are included [59.Hedges L. Olkin I. Vote-counting methods.in: Statistical Methods for Meta-Analysis. Academic Press, 1985: 48-52Crossref Google Scholar]. Therefore, vote-counting is especially prone to miss small effects if the average study is underpowered. Also, the selection of the cut-off is left to the analyst and may be difficult to justify. Ultimately, the assessment of the biological relevance needs to be done with alternative methods. While effect size procedures have clear advantages and should be given preference, vote-counting may still be an important and legitimate analysis tool [60.Bushman B.J. Wang M.C. Vote-counting procedures in meta-analysis.in: Cooper H. The Handbook of Research Synthesis and Meta-Analysis. Russell Sage Foundation, 2009: 207-220Google Scholar]. Most obviously, this is the case when effect sizes are not reported and the available raw data do permit the calculation of effect size estimates. In any case, a strong vote-counting result can help researchers to substantiate their research hypothesis or to detect novel interactions. In genome regulation studies, the situation can be more cumbersome. For example, in semi-quantitative ChIP-seq studies, effect sizes calculated from the number of aligned reads to a genomic locus (e.g., fold enrichments) are hard to compare across laboratory environments (e.g., because of different instruments or protocol variations) and challenging to interpret. Therefore, such studies are designed to merely establish whether a certain signal is present or absent at a specific locus. In our view, performing optimal effect size-based meta-analyses for next-generation sequencing-based experiments would require the rigorous use of spike-ins (usually not available) and a complete recalculation (e.g., read preprocessing, alignment, peak or differential expression calling) based on the raw data if it is available. Such practical considerations underscore the potential usability of badly reputed vote-counting strategies. While it is clear that vote-counting results need to be interpreted carefully, we believe that they are indispensable tools to summarize available data, develop new hypotheses, and trigger validation experiments. Recent work has proposed more sophisticated forms of vote-counting by combining the counts with effect size estimates [60.Bushman B.J. Wang M.C. Vote-counting procedures in meta-analysis.in: Cooper H. The Handbook of Research Synthesis and Meta-Analysis. Russell Sage Foundation, 2009: 207-220Google Scholar]. With transcriptional regulation by the tumor suppressor p53 serving as a prominent example (Box 2), many meta-analysis studies integrate transcriptomic data obtained from microarray and RNA-seq experiments. Another example of a frequently studied system for which there are many public data available is the oscillating expression during the cell division cycle. The first transcriptome analysis of the cell cycle proposed stratifying DEGs into five groups based on the timing of their maximal expression [23.Whitfield M.L. et al.Identification of genes periodically expressed in the human cell cycle and their expression in tumors.Mol. Biol. Cell. 2002; 13: 1977-2000Crossref PubMed Scopus (1208) Google Scholar]. Meta-analyses of multiple transcriptomic datasets on cell cycle-dependent gene regulation later revealed that the five initial groups were not sufficiently robust (i.e., many genes were not reproducibly identified to belong to one distinct group) and helped to reconcile the cell cycle expression groups from five to only two groups, which were more robust [24.Fischer M. et al.Integration of TP53, DREAM, MMB-FOXM1 and RB-E2F target gene analyses identifies cell cycle gene regulatory networks.Nucleic Acids Res. 2016; 44: 6070-6086Crossref PubMed Scopus (200) Google Scholar,25.Giotti B. et al.Meta-analysis reveals conserved cell cycle transcriptional network across multiple human cell types.BMC Genomics. 2017; 18: 30Crossref PubMed Scopus (18) Google Scholar]. This step was instructive for solving the molecular mechanisms that coordinate the precise timing of cell cycle genes [26.Fischer M. Müller G.A. Cell cycle transcription control: DREAM/MuvB and RB-E2F complexes.Crit. Rev. Biochem. Mol. Biol. 2017; 52: 638-662Crossref PubMed Scopus (148) Google Scholar,68.Fischer M. et al.Coordinating gene expression during the cell cycle.Trends Biochem. Sci. 2022; 47: 1009-1922https://doi.org/10.1016/j.tibs.2022.06.007Abstract Full Text Full Text PDF PubMed Scopus (28) Google Scholar]. A third popular example concerns the gene expression differences between species and tissues with important ramifications for model organisms. Early transcriptomic studies found that gene expression is better conserved on a tissue level across species than between tissues within a species [27.Brawand D. et al.The evolution of gene expression levels in mammalian organs.Nature. 2011; 478: 343-348Crossref PubMed Scopus (791) Google Scholar, 28.Barbosa-Morais N.L. et al.The evolutionary landscape of alternative splicing in vertebrate species.Science. 2012; 338: 1587-1593Crossref PubMed Scopus (660) Google Scholar, 29.Merkin J. et al.Evolutionary dynamics of gene and isoform regulation in mammalian tissues.Science. 2012; 338: 1593-1599Crossref PubMed Scopus (614) Google Scholar]. Later, this concept was challenged by an even larger investigation of transcriptomes reporting that gene expression clusters by species instead of tissues [30.Lin S. et al.Comparison of the transcriptional landscapes between human and mouse tissues.Proc. Natl. Acad. Sci. U. S. A. 2014; 111: 17224-17229Crossref PubMed Scopus (261) Google Scholar]. The discordant conclusions were later resolved by a meta-analysis of the joint transcriptomic data, revealing that major tissues indeed have conserved transcriptional programs more similar between the same tissue from different species than between different tissues from the same species. However, some groups of tissues are inherently more similar to each other and in these cases, differences are larger between species than between those tissues [31.Sudmant P.H. et al.Meta-analysis of RNA-seq expression data across species, tissues and studies.Genome Biol. 2015; 16: 287Crossref PubMed Scopus (77) Google Scholar].Box 2Example case: transcription factor p53In recent years, research into the function of the transcription factor and tumor suppressor p53 has led to meta-analyses that have helped address long-standing questions, uncover its regulatory network, and identify nonreproducible results [61.Fischer M. Census and evaluation of p53 target genes.Oncogene. 2017; 36: 3943-3956Crossref PubMed Scopus (515) Google Scholar, 62.Sullivan K.D. et al.Mechanisms of transcriptional regulation by p53.Cell Death Differ. 2018; 25: 133-143Crossref PubMed Scopus (270) Google Scholar, 63.Sammons M.A. et al.Tumor suppressor p53: from engaging DNA to target gene regulation.Nucleic Acids Res. 2020; 48: 8848-8869Crossref PubMed Scopus (33) Google Scholar]. Similar to other transcription factors, numerous genes are up- or downregulated when the activity of p53 is altered. Typically, only a fraction of genes are regulated directly by the transcription factor, while the remainder are controlled indirectly through other means. While transcription factors may facilitate the activation or repression of transcription, the actual effect on a specific gene may depend on the (epi)genomic context, such as cell state or cell type-specific cofactor binding. For many years, p53 was thought to be an ambiguous transcription factor that up- and downregulates genes depending on such a context. However, deciphering this 'context code' remained challenging [61.Fischer M. Census and evaluation of p53 target genes.Oncogene. 2017; 36: 3943-3956Crossref PubMed Scopus (515) Google Scholar]. High-throughput analyses integrating p53 effects on the transcriptome with genome-wide DNA binding data provided contradictory results. While some studies reported that p53 was binding at genes both up- and downregulated by p53 [64.Wei C.L. et al.A global map of p53 transcription-factor binding sites in the human genome.Cell. 2006; 124: 207-219Abstract Full Text Full Text PDF PubMed Scopus (956) Google Scholar,65.Nikulenkov F. et al.Insights into p53 transcriptional function via genome- wide chromatin occupancy and gene expression analysis.Cell Death Differ. 2012; 19: 1992-2002Crossref PubMed Scopus (156) Google Scholar], other studies indicated that p53 binding did not occur at genes downregulated after p53 induction [66.Kenzelmann Broz D. et al.Global genomic profiling reveals an extensive p53-regulated autophagy program contributing to key p53 responses.Genes Dev. 2013; 27: 1016-1031Crossref PubMed Scopus (319) Google Scholar,67.Schlereth K. et al.Characterization of the p53 cistrome – DNA binding cooperativity dissects p53's tumor suppressor functions.PLoS Genet. 2013; 9e1003726Crossref PubMed Scopus (72) Google Scholar]. Ultimately, it required meta-analyses of microarray and RNA-seq data on p53-dependent gene expression in conjunction with p53-ChIP-seq data to clear the mist [24.Fischer M. et al.Integration of TP53, DREAM, MMB-FOXM1 and RB-E2F target gene analyses identifies cell cycle gene regulatory networks.Nucleic Acids Res. 2016; 44: 6070-6086Crossref PubMed Scopus (200) Google Scholar,32.Verfaillie A. et al.Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic.Genome Res. 2016; 26: 882-895Crossref PubMed Scopus (48) Google Scholar,33.Nguyen T.-A.T. et al.Revealing a human p53 universe.Nucleic Acids Res. 2018; 46: 8153-8167Crossref PubMed Scopus (51) Google Scholar,35.Fischer M. et al.The transcription factor p53: not a repressor, solely an activator.Cell Cycle. 2014; 13: 3037-3058Crossref PubMed Scopus (100) Google Scholar]. The meta-analyses revealed that many p53 binding signals (ChIP-seq) near genes downregulated by p53 were nonreproducible and more likely represented false positives in the respective studies. As a consequence, all meta-analysis studies amounted to similar conclusions: binding of p53 was significantly enriched only at genes upregulated by p53 and depleted at genes downregulated by p53 (see Figure 1 in main text). Th